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CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

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The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is an appro ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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Online Transaction Processing (OLTP) deployments are migrating from on-premise to cloud settings in order to exploit the elasticity of cloud infrastructure which allows them to adapt to workload variations. However, cloud adaptation comes at the cost of re ...

ASSOC COMPUTING MACHINERY2020

Ecological surveys increasingly rely on large‐scale image datasets, typically terabytes of imagery for a single survey. The ability to collect this volume of data allows surveys of unprecedented scale, at the cost of expansive volumes of photo‐interpretati ...

2020

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

Michele Ceriotti, Félix Benedito Clément Musil, Michael John Willatt

We present a scheme to obtain an inexpensive and reliable estimate of the uncertainty associated with the predictions of a machine-learning model of atomic and molecular properties. The scheme is based on resampling, with multiple models being generated ba ...

2019

A Machine-Learning Framework for Automating Well-Log Depth Matching

Timon Zimmermann

Depth matching well logs acquired from multiple logging passes in a single well has been a longstanding challenge for the industry. The existing approaches employed in commercial platforms are typically based on classical cross-correlation and covariance m ...

SOC PETROPHYSICISTS & WELL LOG ANALYSTS-SPWLA2019

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Xiaoyu Qu

Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM ...

2018

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

David Atienza Alonso, Marina Zapater Sancho, Ali Pahlevan, Xiaoyu Qu

2017

Designing a Virtual Runtime for FPGA Accelerators in the Cloud

Paolo Ienne, Mikhail Asiatici, Nithin George

FPGAs can provide high performance and energy efficiency to many applications; therefore, they are attractive computing platforms in a cloud environment. However, FPGA application development requires extensive hardware design knowledge which significantly ...

Ieee2016

The IX Operating System: Combining Low Latency, High Throughput, and Efficiency in a Protected Dataplan

Edouard Bugnion, Christos Kozyrakis, Georgios Prekas, Mia Primorac, Ana Klimovic

The conventional wisdom is that aggressive networking requirements, such as high packet rates for small messages and μs-scale tail latency, are best addressed outside the kernel, in a user-level networking stack. We present ix, a dataplane operating system ...

Association for Computing Machinery2016

VNToR: Network Virtualization at the Top-of-Rack Switch

Edouard Bugnion, Georgios Ioannidis, Sam David Whitlock, Jonas Fietz

Cloud providers typically implement abstractions for net- work virtualization on the server, within the operating sys- tem that hosts the tenant virtual machines or containers. Despite being flexible and convenient, this approach has funda- mental problems ...

2016